Cumulative Sum Charts and Charting for Quality Improvement

By E. Umble. Technometrics, 2001

498 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ee1a2ad3d368a88d13a59954bbd795e83c41c309

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Self-starting control chart for simultaneously monitoring process mean and variance (2010, methodology): “…methods, which have been developed accordingly, that update the parameter estimators along with new observations and simultaneously monitor the process to see whether the process is in control or not (Hawkins 1987, Hawkins and Olwell 1998, Quesenberry 1991, 1995, Sullivan and Jones 2002).”
Conditional and marginal performance of the Poisson CUSUM control chart with parameter estimation (2007, methodology): “…effect of parameter estimation on the CUSUM performance for normal observations was studied in the literature, (see, e.g. Bagshaw and Johnson 1975, Hawkins and Olwell 1998, Jones et al. 2004), to the best of our knowledge effect of estimation on the Poisson CUSUM performance has not been…”
Control Charts for Poisson Count Data with Varying Sample Sizes (2010, methodology): “Yashchin (1989) and Hawkins and Olwell (1998) previously recommended using this weighted CUSUM statistic, but only Mei et al. (2010) studied the properties of the chart. Mei et al. (2010) also proposed two other adaptations of the CUSUM chart.”
The computation of average run length and average time to signal: an overview (2014, methodology): “Because the CUSUM statistics are obtained based on a likelihood ratio test [7], it is well known that if the IC parameters, say μ and σ , are assumed to be known, the CUSUM with reference value k = δ/2 is optimal for detecting this shift [33–36].”
The Changepoint Model for Statistical Process Control (2003, methodology): “The self-starting cusum chart was developed as a way of setting up a cusum control without the demand for a large Phase I sample to estimate parameters (see Hawkins (1987) and Hawkins and Olwell (1998)).”
Distribution-free monitoring of univariate processes (2011, methodology): “See, e.g., Hawkins and Olwell (1998), Montgomery (2009), Woodall (2000), and Yeh et al. (2004) for related discussion.”
Detecting mean increases in Poisson INAR(1) processes with EWMA control charts (2011, background): “The most popular approaches for modeling overdispersion are the negative binomial (NB) distribution, [9,19], and the generalized Poisson (GP) distribution [5,10].”
The Poisson INAR(1) CUSUM chart under overdispersion and estimation error (2011, methodology): “From the tabulated results, it becomes clear that, in general, the standard Poisson INAR(1) CUSUM chart as defined by Equation (2) is quite sensitive to overdispersion, a phenomenon well known also for the conventional CUSUM (Hawkins and Olwell, 1998, pp. 66, 119).”
The Use of Control Charts in Health-Care and Public-Health Surveillance (2006, methodology): “For information on this topic, the reader is referred to Hutwagner et al. (1997), O’Brien and Christie (1997), Farrington and Beale (1998), Farrington et al.”
Effects of Parameter Estimation on Control Chart Properties: A Literature Review (2006, background): “He found that the marginal ARL is not a sufficient measure and proposed an alternative measure based on the ratio of the ARL to the SDRL. Hawkins and Olwell (1998) discussed briefly the effect of parameter estimation on CUSUM charts for individual data points collected over time.”
A Change-Point Model for a Shift in Variance (2005, background): “For a more complete exposition of CUSUMs in general and variance CUSUMs in particular, see Hawkins and Olwell (1998).”
The geometric CUSUM chart with sampling inspection for monitoring fraction defective (2001, methodology): “For a discussion of these ARL concepts, refer to Taylor (1968), Crosier (1986), Lucas & Saccucci (1990), Hawkins & Olwell (1998), and Zimmer et al. (2000).”
CUSUM and EWMA multi-charts for detecting a range of mean shifts (2007, background): “For comparison, the in-control ARL (ARL0) of all candidate charts are forced to be equal, and to correspond to the same level of type I error.”
A study of EWMA chart with transformed exponential data (2007, methodology): “Hawkins and Olwell ( 1998 ) also studied the monitoring of Weibull-distributed TBE data with a cumulative sum (CUSUM) chart, and Zhang and Chen ( 2004 ) worked on the EWMA for censored Weibull lifetimes.”
An Examination of the Robustness to Non Normality of the EWMA Control Charts for the Dispersion (2005, background): “This deficiency can be resolved by using the moving range ( Hawkins and Olwell, 1998, p. 82 ) or by calculating at each point in time (observation) an estimate of the mean (MacGregor and Harris, 1993).”
CONTROL CHART FOR MONITORING NONPARAMETRIC PROFILES WITH ARBITRARY DESIGN (2009, background): “In addition, we focus on the steady-state OC ARL behavior of each chart (cf., Hawkins and Olwell 1998), and assume that τ = 30 (cf., the OC model (6)).”